Indian Journal of Science and Technology
Year: 2019, Volume: 12, Issue: 40, Pages: 1-9
Awais Ahmed Shujrah1, Samina Rajper1 and Awais Khan Jumani2,*
1 Department of Computer Science, Shah Abdul Latif University, Khairpur Mirs, Sindh, Pakistan; [email protected], [email protected]
2 Faculty of Science and Technology, ILMA University, Karachi, Sindh, Pakistan; [email protected]
Motivations: E-Learning is more popular in today’s era; most of the institutes have E-learning systems and also provide distance learning education. On the other hand, some of the main reasons for student’s failures in academics are lack of communication with teachers. Problem: This type of gap between student and teachers make big flaws in academics. Due to this type of gap, the student cannot take an interest in any academic course. This study aims to measure the level of student’s interest in any online course using Support Vector Machine (SVM). Objectives: The main objective of this research is to measure the student’s interest level in online courses and classify the e-learners on the basis of their level of interest in the course. Methods: The information from the spring semester of 2019 from the Department of Computer Science and encompass 597 students and 39 diverse courses. SVM technique has been used to filter the data and collected sequence data especially data processing, clustering, classification, regression, visualization, and feature selection. Findings: In the last, our system can detect the quantity of pages per session. In this system, we accomplish the different levels of students with their learning styles. The teacher can also be able to improve themselves with this system. The teacher may change the teaching methodology with their students. Application: It has been created a platform for classifying the students with weblogs interest in a particular course. After classifying the e-learners on the basis of their interest in a particular course will help the teacher/ E-teacher to change or improve the instructional strategy to develop/improve the level of interest of students.
Keywords: Machine Learning, Support Vector Machine, E-Learning, Web Logs, Clusters
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